1. Field of the Invention
This invention relates generally to a defect classification methodology in a semiconductor manufacturing testing system. More specifically, this invention relates to an automatic defect classification methodology that stores the most ideal images for each type of defect in a master library of images. Even more specifically, this invention relates to an automatic defect classification methodology that stores the most ideal images for each type of defect in a master library of images that is accessible and readable by any type of automatic defect classification system.
2. Discussion of the Related Art
In order to remain competitive, a semiconductor manufacturer must continuously increase the performance of the semiconductor integrated circuits being manufactured and at the same time, reduce the cost of the semiconductor integrated circuits. Part of the increase in performance and the reduction in cost of the semiconductor integrated circuits is accomplished by shrinking the device dimensions and by increasing the number of circuits per unit area on an integrated circuit chip. Another part of reducing the cost of a semiconductor chip is to increase the yield. As is known in the semiconductor manufacturing art, the yield of chips (also know as die) from each wafer is not 100% because of defects to die during the manufacturing process. The number of good chips obtained from a wafer determines the yield. As can be appreciated, chips that must be discarded because of a defect increases the cost of the remaining usable chips.
A single semiconductor chip can require numerous process steps such as oxidation, etching, metallization, ion implantation, thermal annealing, and wet chemical cleaning. These are just a few of the many types of process steps involved in the manufacture of a semiconductor chip. Some of these process steps involve placing the wafer on which the semiconductor chips are being manufactured into different tools during the manufacturing process. The optimization of each of these process steps requires an understanding of a variety of chemical reactions and physical processes in order to produce high performance, high yield circuits. The ability to view and characterize the surface and interface layers of a semiconductor chip in terms of their morphology, chemical composition and distribution is an invaluable aid to those involved in research and development, process, problem solving, and failure analysis of integrated circuits. A major part of the analysis process is to determine if defects are caused by one of the process tools, and if so, which tool caused the defects.
As the wafer is placed into different tools during manufacture, each of the tools can produce different types of particles that drop onto the wafer and cause defects that have the potential to "kill" a die and decrease the yield. In order to develop high yield semiconductor processes and to improve existing ones, it is important to identify the sources of the various particles that cause defects and then to prevent the tools from dropping these particles onto the wafer while the wafers are in the tools.
In order to be able to quickly resolve process or equipment issues in the manufacture of semiconductor products, a great deal of time, effort and money are being expended by semiconductor manufacturers to capture and classify defects encountered in the manufacture of semiconductor products. Once a defect is detected, properly described, and classified, effort can begin to resolve the cause of the defect and to eliminate the cause of the defect. The biggest problem that faced the semiconductor manufacturers and one of the most difficult to solve was the problem associated with the training and maintenance of a cadre of calibrated human inspectors who can classify all types of defects consistently and without error. This problem was mainly caused by unavoidable human inconsistency and as a solution to this problem, Automatic Defect Classification (ADC) systems were developed.
One such system for automatically classifying defects consists of the following methodological sequence. Gather a defect image from a review station. View the defect image and assign values to elemental descriptor terms called predicates that are general descriptors such as roundness, brightness, color, hue, graininess, etc. Assign a classification code to the defect based upon the values of all the predicates. A typical ADC system could have 40 or more quantifiable qualities and properties that can be predicates. Each predicate can have a specified range of values and a typical predicate can have a value assigned to it between 1 and 256. The range of values that can be assigned to a predicate is arbitrary and can be any range of values. In this example, a value of 1 could indicate that none of the value is present and a value of 256 would indicate that the quality represented by the predicate is ideal. For example, a straight line would have a value of 1 for the predicate indicating roundness, whereas a perfect circle would have a value of 256 for the same predicate. The classification code for each defect is determined by the system from the combination of all the predicate values assigned to the defect. The goal of the ADC system is to be able to uniquely describe all the defect types, in such a manner that a single classification code can be assigned to a defect that has been differentiated from all other types of defects. This is accomplished by a system administrator who trains an artificial intelligence system to recognize various combinations and permutations of the 40 or more predicates to assign the same classification code to the same type of defect. This would result in a highly significant statistical confidence in the probability that the defect, and all other defects of the same type or class, will always be assigned the same classification code by the ADC system. This is done by performing a "best-fit" calculation against all assigned classification codes. If the fit is not good enough, the system will assign an "unknown" code, which means the system needs further training for that device/layer/defect. Once the classification code for a particular defect is determined and assigned, the predicate values that pertain to that defect and which were used to determine the classification code are not saved. In most cases, the only value saved in the database is the classification code. In rare instances, the image of the defect is saved. For these early ADC systems, it was found that if the classification codes needed to be modified to further differentiate between defects it could not be done because none of the information necessary to determine new classification codes was available. The only times the classification codes could be modified were the rare cases in which the image of the defect was preserved.
However, another problem soon emerged. This problem is that the classification codes were tool dependent. The classification codes are determined by predicates, however, the predicates are unique to each tool and the associated ADC system that detects, assigns values to the predicates, and assigns a classification code to each defect. As the manufacturing process becomes more complex, different tools are utilized to detect defects and assign classification codes to the defects. Because the predicates are unique to each tool, the accumulated knowledge in each tool/ADC system is only usable by that tool/ADC system. Each new tool whether it is a new model from the same manufacturer or a new tool from a different manufacturer may have different predicates that need to be calibrated.
Therefore, what is needed is a methodology that utilizes common defect information that is accessible and usable by all tools.